num_classes=8 creates off-by-one error with number of predictions

[villanuevab]

Thanks for this implementation. In ssd_common.py using default params we have num_classes=8; this is consistent with what is in datasets/kitti*. I understand why no_annotations_label=9, and think I see that these labels > num_classes are handled in tf_bboxes_filter_labels. However, in ssd_common.py we have:

# Predictions, removing first void class.
sub_predictions = predictions_layer[:, :, 1:]
idxes = np.where(sub_predictions > threshold)
classes = idxes[-1]+1
scores = sub_predictions[idxes]

Does this cause an off-by-one error? I understand that the 0th index represents 'none': (0, 'Background'), but in this case should we have specified num_classes=9? Otherwise we do not consider the 'misc' class for KITTI ('Misc': (8, 'Misc'), since label < num_classes is false in this case)?

Given we have num_cls_pred = num_anchors * num_classes predictions (see ssd_vgg_300.py), this means that if num_classes=8 then subpredictions would only consider 7 classes. Is this correct?

[xinyugaotudelft]

Hi Blanca,

Interesting question! May I ask how did you solve this in the end?

Much thanks